Applications of Artificial Intelligence and Remote Sensing in Environmental and Agricultural Engineering: A Comprehensive Review
DOI:
https://doi.org/10.5281/zenodo.18061813Keywords:
Artificial intelligence, Remote sensing, Environmental monitoring, Sustainable engineeringAbstract
Artificial Intelligence (AI) and remote sensing technologies have transformed the landscape of environmental and agricultural engineering. These technologies enable the monitoring, analysis, and prediction of complex natural processes at multiple spatial and temporal scales. This review summarizes current advances in the integration of AI algorithms and remote sensing data for precision agriculture, environmental monitoring, and resource management. Emphasis is placed on the use of machine learning (ML) and deep learning (DL) models for crop yield prediction, soil salinity mapping, water resource optimization, and climate impact assessment. Challenges related to data quality, computational cost, and model generalization are discussed. Finally, the paper highlights future directions for AI-driven sustainable engineering applications.
References
Boukita, H., El Ammari, M., Elwahab, F., El Bahja, F., Oudghiri, M., Brhadda, N., Ziri, R., 2025. Invasive plants in urban settings: A systematic review and bibliometric analysis of trends, gaps, and future implications. Trees, Forests and People, 22: 101051.
Chen, Q., Timmermans, J., Wen, W., van Bodegom, P.M., 2023. Ecosystems threatened by intensified drought with divergent vulnerability. Remote Sensing of Environment, 289: 113512.
Elhag, M., 2019. Evaluation of different soil salinity mapping approaches in arid environments using remote sensing techniques. Environmental Monitoring and Assessment, 191(1): 35.
Elhag, M., Bahrawi, J.A., 2017. Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques. Geoscientific Instrumentation, Methods and Data Systems, 6(1):149 158.
Elwahab, F., Sedki, M., Brhadda, N., Ziri, R., 2023. Review of agronomic and genetic diversity of Moroccan rice varieties, and their resistance to blast disease (Pyricularia oryzae). Agronomy Research, 21(S1): 267 290.
Elwahab, F., Sedki, M., Sock, M., Lagnigui Clément, K., Boukita, H., Brhadda, N., Ziri, R., 2025. Assessment of the agronomic performance of newly introduced rice varieties in Morocco. International Journal of Agriculture and Biology, 31(5): 311–320.
Espinel, R., Herrera Franco, G., Rivadeneira García, J.L., Escandón Panchana, P., 2024. Artificial ıntelligence in agricultural mapping: A review. Agriculture, 14(7): 1071.
FAO, 2022. FAO Strategy on Climate Change 2022 2031. Food and Agriculture Organization of the United Nations, Rome.
Gholizadeh, M., Melesse, A.M., Reddi, L., 2020. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 20(21): 5938.
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2019. Prevalence of multiple forest disturbances and impact on vegetation regrowth from interannual Landsat time series (1985–2015). Remote Sensing of Environment, 233: 111403.
Ismaili, M., Krimissa, S., Namous, M., Htitiou, A., Abdelrahman, K., Fnais, M.S., Lhissou, R., Eloudi, H., Faouzi, E., Benabdelouahab, T., 2023. Assessment of soil suitability using machine learning in arid and semi-arid regions. Agronomy, 13(1): 165.
Kamilaris, A., Prenafeta Boldú, F.X., 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147: 70 90.
Katkani, D., Babbar, A., Mishra, V.K., Trivedi, A., Tiwari, S., Kumawat, R.K., 2022. A review on applications and utility of remote sensing and geographic ınformation systems in agriculture and natural resource management. International Journal of Environment and Climate Change, 12(4): 1-18.
Kerner, N., Specht, J., Lobell, D.B., 2022. Combining satellite imagery and machine learning to map maize yields in smallholder systems. Remote Sensing of Environment, 268: 112760.
Ma, H., Chen, Y., Zhu, J., Fan, R., Chen, Z., Meng, L., Liu, T., 2023. Alleviating the adverse impacts of high influent ammonium on an activated sludge system by adding Fe salts and ballasted media. Journal of Cleaner Production, 384: 135682.
Mazarei, R., Mohammadi, A.S., Ebrahimian, H., Naseri, A., 2021. Temporal variability of infiltration and roughness coefficients and furrow irrigation performance under different inflow rates. Agricultural Water Management, 245: 106465.
Mazarei, R., Soltani Mohammadi, A., Ebrahimian, H., Naseri, A., 2021. Temporal variability of infiltration and roughness coefficients and furrow irrigation performance under different inflow rates. Agricultural Water Management, 245: 106465.
Merchant, M.A., Obadia, M., Brisco, B., DeVries, B., Berg, A., 2022. Applying machine learning and time-series analysis on Sentinel-1A SAR/InSAR for characterizing Arctic tundra hydro-ecological conditions. Remote Sensing, 14(5): 1123.
Nevavuori, P., Narra, N., Lipping, T., 2019. Crop yield prediction with deep convolutional neural networks. Computers and Electronics in Agriculture, 163: 104859.
Ohyama, K., Fujioka, J., Sato, T., Matsuo, T., 2023. System for determining the job status of individual laborers in a large-scale greenhouse. Computers and Electronics in Agriculture, 210: 108123.
Rana, R., Kalia, A., Boora, A., Alfaisal, F.M., Saad Alharbi, R., Berwal, P., Alam, S., Khan, M. A., Qamar, O., 2023. Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment. Water, 15(22): 3919.
Rowley, A., Karakuş, O., 2023. Predicting air quality via multimodal AI and satellite imagery. Remote Sensing of Environment, 293: 113609.
Sarker, O.V.Y., Ikram, M.S., Moni, S.A., Gunasekar, G., A, A., A.R, K., Elwahab, F., Sedki, M., Abdelaziz, C., Sana El, M., Brhadda, N., Ziri, R., 2025. Sustainable agriculture through digital and biotechnological innovations.
Sothe, C., Gonsamo, A., Arabian, J., Snider, J., 2021. Large-scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations. Geoderma, 404: 115402.
Souissi, R., Zribi, M., Corbari, C., Mancini, M., Muddu, S., Tomer, S.K., Upadhyaya, D.B., Al Bitar, A., 2022. Integrating process related information into an artificial neural network for root zone soil moisture prediction. Hydrology and Earth System Sciences, 26: 3263 3297
Wang, X., Zhang, J., Xun, L., Wang, J., Wu, Z., Henchiri, M., Zhang, S., Zhang, S., Bai, Y., Yang, S., Li, S., Yu, X., 2022. Evaluating the effectiveness of machine learning and deep learning models combined time series satellite data for multiple crop types classification over a large scale region. Remote Sensing, 14(10): 2341.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 ISPEC JOURNAL OF SCIENCE INSTITUTE
This work is licensed under a Creative Commons Attribution 4.0 International License.
